import tensorflow as tf
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import datetime

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util

from object_detection.utils import visualization_utils as vis_util

DETECT_PATH = 'object_detection'
MODEL_NAME = 'models/ssd_mobilenet_v1/inference' # 'ssd_mobilenet_v1_coco_11_06_2017'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb')

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'pascal_label_map.pbtxt') # 'mscoco_label_map.pbtxt')

sess = None
detection_graph = None

ONEHOTNUM = 90
NUM_CLASSES = 20
threshold = 0.7
limit = 10
dataset = []
dataurls = []
category_index = []

def Object_Detection_init():
    global dataset
    global dataurls
    global category_index
    global sess
    global detection_graph
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    detection_graph.as_default()
    sess = tf.Session(graph=detection_graph)
    dataset = np.load('onehot_db.npy')
    dataurls = np.load('urls_db.npy')
    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)
    return sess, detection_graph


def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


def Msearch(imageList):
    begin1 = datetime.datetime.now()
    resulturl = []
    onehots = []
    for image_path in imageList:
        image = Image.open(os.path.join(PATH_TO_TEST_IMAGES_DIR, image_path))
        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        image_np = load_image_into_numpy_array(image)

        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
        hashes = detection_graph.get_tensor_by_name('detection_hashes:0')
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')

        # Actual detection.
        begin = datetime.datetime.now()
        print("run begin")
        (boxes, scores, hashes, classes, num_detections) = sess.run(
            [boxes, scores, hashes, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        end = datetime.datetime.now()
        print(end - begin)
        # onehot = compute_onehot(scores, classes)
        # onehot = np.float32(hashes > 0.5)
        onehot = hashes
        if onehot.size == 0:
            continue
        else:
            onehots.append(np.float32(onehot > 0.5))
            # vis_util.visualize_boxes_and_labels_on_image_array(
            #     image_np,
            #     np.squeeze(boxes),
            #     np.squeeze(classes).astype(np.int32),
            #     np.squeeze(scores),
            #     category_index,
            #     use_normalized_coordinates=True,
            #     line_thickness=8)
            # image = Image.fromarray(image_np)
            # image.save(image_path)
    if len(onehots) == 0:
        return []
    results = DatasetRanking(np.vstack(onehots), dataset)
    print(results)
    end1 = datetime.datetime.now()
    print(end1 - begin1)

    return imageList, [dataurls[id] for v, id in results]


def compute_onehot(scores, classes):
    index = classes[np.where(scores > threshold)]
    index = index.astype(int)
    out = np.zeros((index.size, ONEHOTNUM))
    # one-hot
    for row in range(out.shape[0]):
        out[row][index[row] - 1] = 1
    return out > 0


def DatasetRanking(onehots, dataset):
    results = {}
    for i in range(dataset.shape[0]):
        dist = getDistFormOnehots(onehots[0], dataset[i][0])
        results[i] = dist
    results = sorted([(v, k) for (k, v) in results.items()])
    return results[:limit]

def getDistFormOnehots(onehots1, onehots2):
    if onehots2.size == 0:
        return 99
    dist = 0
    for row in onehots1:
        if onehots2.ndim > 1:
            dist += np.sum(np.bitwise_xor(np.int64(row), np.int64(onehots2)), axis=1).min()
        else:
            dist += np.sum(np.bitwise_xor(np.int64(row), np.int64(onehots2)), axis=1).min()
    return dist

if __name__ == '__main__':
    sess, detection_graph = Object_Detection_init()
    PATH_TO_TEST_IMAGES_DIR = 'C:\\Users\\谷雪松\\Documents\\gxs\\datatsets\\VOC2012\\VOCdevkit\\VOC2012\\testDetectionJPEGImages'
    TEST_IMAGE_PATHS = os.listdir(PATH_TO_TEST_IMAGES_DIR)
    imageList = TEST_IMAGE_PATHS
    imageList, dataurls = Msearch(imageList)
    pass